Tan Guo1, Ya-Jun Ma2, Rachel A High3, Qingbo Tang4, Jonathan H Wong5, Michal Byra6, Adam C Searleman7, Sarah C To8, Lidi Wan9, Nicole Le10, Jiang Du11, Eric Y Chang12. 1. Department of Radiology, Beijing Hospital, Beijing, China; Department of Radiology, University of California, San Diego, CA, United States. Electronic address: guotan369@hotmail.com. 2. Department of Radiology, University of California, San Diego, CA, United States. Electronic address: yam013@ucsd.edu. 3. Department of Radiology, University of California, San Diego, CA, United States; Research Service, VA San Diego Healthcare System, San Diego, CA, United States. Electronic address: rachelahigh@gmail.com. 4. Department of Radiology, University of California, San Diego, CA, United States; Research Service, VA San Diego Healthcare System, San Diego, CA, United States. Electronic address: q1tang@ucsd.edu. 5. Department of Radiology, University of California, San Diego, CA, United States; Research Service, VA San Diego Healthcare System, San Diego, CA, United States. Electronic address: jhw033@ucsd.edu. 6. Department of Radiology, University of California, San Diego, CA, United States; Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland. Electronic address: byra.michal@gmail.com. 7. Department of Radiology, University of California, San Diego, CA, United States. Electronic address: asearleman@ucsd.edu. 8. Department of Radiology, University of California, San Diego, CA, United States; Research Service, VA San Diego Healthcare System, San Diego, CA, United States. Electronic address: sarahcto23@gmail.com. 9. Department of Radiology, University of California, San Diego, CA, United States. Electronic address: qldxt_wld1993@163.com. 10. Department of Radiology, University of California, San Diego, CA, United States; Research Service, VA San Diego Healthcare System, San Diego, CA, United States. Electronic address: lenicolem@gmail.com. 11. Department of Radiology, University of California, San Diego, CA, United States; Research Service, VA San Diego Healthcare System, San Diego, CA, United States. Electronic address: jiangdu@ucsd.edu. 12. Department of Radiology, University of California, San Diego, CA, United States; Research Service, VA San Diego Healthcare System, San Diego, CA, United States. Electronic address: ericchangmd@gmail.com.
Abstract
PURPOSE: Quantitative imaging methods could improve diagnosis of rotator cuff degeneration, but the capability of quantitative MR and US imaging parameters to detect alterations in collagen is unknown. The goal of this study was to assess quantitative MR and US imaging measures for detecting abnormalities in collagen using an in vitro model of tendinosis with biochemical and histological correlation. METHOD: 36 pieces of supraspinatus tendons from 6 cadaveric donors were equally distributed into 3 groups (2 subjected to different concentrations of collagenase and a control group). Ultrashort echo time MR and US imaging measures were performed to assess changes at baseline and after 24 h of enzymatic digestion. Biochemical and histological measures, including brightfield, fluorescence, and polarized microscopy, were used to verify the validity of the model and were compared with quantitative imaging parameters. Correlations between the imaging parameters and biochemically measured digestion were analyzed. RESULTS: Among the imaging parameters, macromolecular fraction (MMF), adiabatic T1ρ, T2*, and backscatter coefficient (BSC) were useful in differentiating between the extent of degeneration among the 3 groups. MMF strongly correlated with collagen loss (r=-0.81; 95% confidence interval [CI]: -0.90,-0.66), while the adiabatic T1ρ (r = 0.66; CI: 0.42,0.81), T2* (r = 0.58; CI: 0.31,0.76), and BSC (r = 0.51; CI: 0.22,0.72) moderately correlated with collagen loss. CONCLUSIONS: MMF, adiabatic T1ρ, and T2* measured and US BSC can detect alterations in collagen. Of the quantitative MR and US imaging measures evaluated, MMF showed the highest correlation with collagen loss and can be used to assess rotator cuff degeneration. Published by Elsevier B.V.
PURPOSE: Quantitative imaging methods could improve diagnosis of rotator cuff degeneration, but the capability of quantitative MR and US imaging parameters to detect alterations in collagen is unknown. The goal of this study was to assess quantitative MR and US imaging measures for detecting abnormalities in collagen using an in vitro model of tendinosis with biochemical and histological correlation. METHOD: 36 pieces of supraspinatus tendons from 6 cadaveric donors were equally distributed into 3 groups (2 subjected to different concentrations of collagenase and a control group). Ultrashort echo time MR and US imaging measures were performed to assess changes at baseline and after 24 h of enzymatic digestion. Biochemical and histological measures, including brightfield, fluorescence, and polarized microscopy, were used to verify the validity of the model and were compared with quantitative imaging parameters. Correlations between the imaging parameters and biochemically measured digestion were analyzed. RESULTS: Among the imaging parameters, macromolecular fraction (MMF), adiabatic T1ρ, T2*, and backscatter coefficient (BSC) were useful in differentiating between the extent of degeneration among the 3 groups. MMF strongly correlated with collagen loss (r=-0.81; 95% confidence interval [CI]: -0.90,-0.66), while the adiabatic T1ρ (r = 0.66; CI: 0.42,0.81), T2* (r = 0.58; CI: 0.31,0.76), and BSC (r = 0.51; CI: 0.22,0.72) moderately correlated with collagen loss. CONCLUSIONS: MMF, adiabatic T1ρ, and T2* measured and US BSC can detect alterations in collagen. Of the quantitative MR and US imaging measures evaluated, MMF showed the highest correlation with collagen loss and can be used to assess rotator cuff degeneration. Published by Elsevier B.V.
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